Pluvial flood modelling of the South East London Resilience Zone in the Community Resilience to Extreme Weather (CREW) Project

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1 Pluvial flood modelling of the South East London Resilience Zone in the Community Resilience to Extreme Weather (CREW) Project ALBERT S. CHEN 1), SLOBODAN DJORDJEVIĆ 1), HAYLEY J. FOWLER 2), AIDAN BURTON 2), CLAIRE WALSH 2), HAMISH HARVEY 2), JIM HALL 2), RICHARD DAWSON 2), GAVIN WOOD 3) 1) Centre for Water Systems, School of Engineering, Computing and Mathematics, University of Exeter 2) School of Civil Engineering and Geosciences, Newcastle University 3) Integrated Environmental Systems Institute, Department of Natural Resources, School of Applied Sciences, Cranfield University Key Words: extreme weather, pluvial flood modelling, event filter Abstract: The project Community Resilience to Extreme Weather' (CREW), funded by the Engineering and Physical Sciences Research Council, has gathered scientists and engineers from different disciplines to develop tools for assessing the changing probability and impacts of extreme weather events. These tools, being developed initially for a study area in the South East London Resilience Zone, will be invaluable to communities exploring options for improving their resilience to the impacts of a changing climate. An interactive web-based information portal is being built to integrate the research results and make them available to end-users. The study area is subject to increasing tidal and fluvial flood risk. In addition, extreme rainfall events are likely to occur more frequently, leading to increased pluvial (surface water) flooding. One component of the CREW project, therefore, concerns pluvial flooding. This work is novel in estimating probabilities of pluvial flooding and accounting for climate change. A spatial-temporal rainfall model of the study area has been developed based on observed weather data. This model provides hourly rainfall fields on a 5km grid suitable for upstream watersheds modelling and 15 minute rainfall field data on a nested 2km grid appropriate for downstream areas hydraulic modelling. Rainfall models for future climate scenarios based on the UK Climate Projections (UKCP09) will also be prepared. Time series of simulated rainfall data are used as input to the Urban Inundation Model (UIM), a 2D non-inertial overland flow model that can be driven by a spatially and temporally varying rainfall input, providing estimates of the probability of pluvial flooding. Computational efficiency of the UIM is improved by omitting minor rainfall events from the simulated rainfall data according to a filtering scheme based on the piped drainage capacity in the study area. In the CREW project the results will be used to estimate flood damages and to develop coping measures. Probabilities of combined extreme weather and environmental events involving flooding, droughts, heat waves, subsidence, wind and lightning will also be investigated. INTRODUCTION The IPCC's Fourth Assessment Report (AR4) (Bernstein et al., 2008) has concluded that global warming is evident from observations of various indices. Extreme weather events (EWEs) such as floods, heat waves and droughts are projected to be more frequent and intense over the 21 st century. The Stern Report on the Economics of Climate Change (Stern, 2007) identifies that even if we could stop all greenhouse gas emissions tomorrow our climate would continue to change due to global warming driven by over a century of manmade emissions. As a consequence, the risk of EWEs is likely to continue increasing over the next half century. Today's weather extremes are likely to become tomorrow's norms. Bates et al. (2008) report a significant change in precipitation attributes since the 1970s and states that heavy rainfall events are very likely to increase over many areas. Following the extreme flood events in 2007, the Pitt Review (Pitt, 2007) urged the UK government to support and encourage community activities likely to improve flood resilience as a means of reducing flood risk. The project Community Resilience to Extreme Weather' (CREW), funded by the Engineering and Physical Sciences Research Council, has gathered scientists and engineers from different disciplines to develop tools for assessing the changing probability and impacts of EWEs in the South East London Resilience Zone (SELRZ). CREW consists of six programme packages for developing and testing a 1

2 range of tools that support a greater understanding of the strategic issues that affect local community resilience to EWEs and provide the basis for the development of effective intervention (coping) measures for local policy makers, households and businesses (SMEs). The research outcomes are integrated into an interactive web-based information portal and made available to end-users. CREW is using five South East London boroughs as case studies, which are subject to increasing tidal and fluvial flood risk (Environment Agency, 2008). In addition, extreme rainfall events are likely to occur more frequently, leading to increased pluvial (surface water) flooding in the SELRZ (Environment Agency, 2007). The main objective of the programme package Severe Weather Events Risk and Vulnerability Estimator (SWERVE) in CREW is developing a physically-based modelling framework to identify probabilities of local EWEs and risk hotspots for combined probabilities of different EWEs, including flooding, droughts, heat waves, subsidence, wind and lightning, for current and future climates at the community-scale as inputs to socio-economic modelling and a GIS toolkit. Here we present a preliminary study based on the simulation approach for the present day climate assessing pluvial flooding in the SELRZ. A stochastic rainfall model is used to generate spatial-temporal rainfall fields at two spatial-temporal resolutions. The first is at a coarser resolution suitable for supporting the modelling of the entire catchments containing the five boroughs. The second is at a finer resolution on a nested grid for the downstream flood-prone areas. The synthetic rainfall data is used as input to a hydraulic model for pluvial flood modelling. The computational expense of the required hydraulic simulations is however prohibitively expensive and so a methodology is devised to identify the events that provide significant extreme events. Restricting the hydraulic simulation to these reduces the computation burden to a feasible level. METHODOLOGY Stochastic simulation of spatial-temporal rainfall fields To account for the climate change effects on pluvial flooding, future rainfall scenarios are first simulated to generate inputs to the surface flood model. A stochastic Spatial-Temporal Neyman-Scott Rectangular Pulses (STNSRP) model (Burton et al., 2008) has been fitted to observed weather data for the study region. This model generates continuous spatial-temporal rainfall fields which are sampled at 63 locations on a regular 5-km grid (Figure 1) and aggregated to provide hourly timeseries. This dataset has suitable time and space resolutions and spatial coverage to provide input for hydrological modelling of the catchments upstream of the study area. Figure 1 The centres and grids of rainfall data sites 2

3 A disaggregation methodology, based on the multi-site rainfall disaggregator developed by Cowpertwait (2004), is then applied. This methodology conceptualizes intra-storm rainfall as arising from short intense and localized raincells occurring with a uniform spatial temporal Poisson process conditioned by the STNSRP model outputs. This model provides disaggregated rainfall fields which are sampled at 23 locations on a 2km grid (Figure 1) and aggregated to provide 15-minute time series. This dataset is suitable input data for the hydraulic model of low-lying urban areas in the study area. The procedure to generate synthetic rainfall fields for future climate scenarios is as follows. Change Factors (e.g. Kilsby et al., 2007) derived from the UK Climate Projections (UKCP09) are applied to the observed weather statistics. The rainfall models are then refitted and may then be used to generate rainfall of arbitrary duration. Many years of rainfall data may then be generated for both the control and future climate scenarios, facilitating the estimation of the frequency of rare hydraulic and hydrological events. A comparison of the control and future frequency of flooding events provides an estimate of the effect of climate change on pluvial flooding. Flood modelling The rainfall hyetographs of selected events are fed to the Urban Inundation Model (UIM; Chen et al., 2007) for pluvial flood modelling. UIM takes spatially and temporally varying precipitation model inputs and solves the two-dimensional non-inertia flow equations for routing the flood movements on the ground surface. The downstream study area is densely urbanised, where sewer systems are essential infrastructure to drain surface runoff. UIM can be integrated with the one-dimensional sewer hydraulic model SIPSON (Djordjević et al., 2005), allowing interactions between surface and sub-surface flows to be studied. Data on sewers is difficult to obtain, and the size of the study area is such that using this facility would lead to very long run times. Sewer capacities are in any case relatively small relative to the flows that develop during the extreme events with which this study is concerned. A simple way to reflect the sewer capacities is deducting the design rainfall intensity directly from the precipitation before feeding into flood modelling. However, the treatment does not allow draining of surface runoff when rainfall stops. An alternative approach is therefore adopted in which a soil infiltration model with no storage capacity limit is used as a surrogate for the drainage system. The sewers are thus assumed to provide their design capacity at all times. Following British Standard BS EN 752 (British Standards Institution, 1998), this capacity is sufficient to avoid surcharging during a rainfall event with 1 in 2 year return period and any duration. Without being affected by flow conditions in sewers, the 60-minute design rainfall intensity is used as the constant drainage rate to reflect operation of sewer networks in urban areas. The upper part of the study area is more rural. Here an infiltration model accounting for soil saturation is used. Event filter The continuous data describes detailed temporal changes in precipitation over a long time series, which also includes plenty of dry periods. In order to reduce simulation time, a pre-processing filter is applied to screen out minor rainfall events that cause no flooding, and select for simulation only those that could overload the sewer systems and result in flooding. As discussed above, the flood model assumes that surface runoff in paved urban areas is drained at a rate up to the design capacity of the sewer network, regardless of the flow conditions in the network. Shorter duration storms of the same return period are more intense. On the other hand, the accumulated rainfall depth is greater for longer storms of a given return period. These relationships are encapsulated in the Wallingford Procedure for the design and analysis of urban drainage systems (Department of Environment, 1981). Specific values for the study area are given in Tables 1 (rainfall intensities) and 2 (total depths). A rainfall event is defined as a continuous duration of which any site with the basin has rainfall accumulation or intensity above the selection criteria. The following filtering steps apply to individual rainfall data sites for screening out the events for flood modelling. 1. Compute the intensity of hourly rainfall for each site to select records that are greater than 1 in 2 year return period (for the sites with finer temporal resolution, the moving average is calculated every 15 minutes). 2. Select from the data of the 23 sites with finer resolution for having rainfall depth higher than 1 in 2 year return period storm to screen out the intense short rainfall events. 3. Extend durations of selected events to cover the concentration time of the whole basin and combine events with short dry gaps. 3

4 Table 1 Rainfall intensities (mm/hour) of various durations and return periods for the study area Duration Return period 1y 2y 5y 10y 20y 30y 50y 100y 15min min min min min min Table 2 Rainfall depth (mm) for various durations and return periods for the study area Duration Return period 1y 2y 5y 10y 20y 30y 50y 100y 15min min min min min min Step (1) selects the events that exceed the sewer capacity in flood modelling. Table 1 and 2 data indicate that intensity decreases as duration increases for the events with same return period. The 60-minute rainfall intensity threshold (16.20mm/hr) implies that the sewer networks are capable to deal with long duration events. Nevertheless, heavy rainfall that concentrates in a very short time period may overload the sewer network quickly and cause surface flooding in the study area (Mayor of London, 2007). Step (1) fails to capture these events (e.g., 15-minute storm with 10-year return period) because the intensity is lowered by hourly-interval-based average (e.g. it is calculated as 15.96mm/hr rather than mm/hr). However, if the intensities are calculated on the 15- or 30-minute interval basis, it seems that even a 15-minute storm with 1-year return period (8.26 mm) would cause serious flooding problem because the equivalent hourly intensity is mm/hr. Although the pulse intensities are greater than mm/hr, the drainage networks would be able to carry intense rainfall for such events because the accumulation is still small. The BS EN 752 suggests 15 minutes as the concentration time for drainage design. To focus on extreme scenarios and disregard minor events, which surface runoff can be drained quickly after rainfall stops, Step (2) applies the 1 in 2 year return period criteria again to 15- and 30-minute accumulate precipitation to screen out the major events by allowing 30 minutes for sewer systems to collect surface runoff. In the basin scale, runoff from upstream catchments will affect downstream areas, and the confluence with the following rainfalls, albeit the intensities are below 2-year return period, could exaggerate flooding situations. To simulate the response of runoff and rainfall combinations, the event needs to be prolonged to allow the upstream peak flow to propagate to downstream outfall. Therefore, Step (3) extends the event duration to cover the concentration time of basin. If a gap between two filtered events is shorter than the concentration time, Step (3) will combine these events as a single one. APPLICATION TO THE STUDY AREA Bexley, Bromley, Croydon, Greenwich and Lewisham are the five boroughs in the case study area. For flood modelling, the study area needs to be extended to the catchment boundary. Figure 2 shows the borough boundaries and the modelling boundary, which is delineated by using the OS Land-Form PROFILE DTM Data with 10m resolution. The total catchment area is 900km 2 with terrain elevation ranging from -3m to 270 m above Ordnance Datum. The main water courses in the area are three tributaries of the Thames, the rivers Wandle, Ravensbourne and Cray. The upstream tributaries of the Wandle pass through the borough Croydon in the west. The Ravensbourne flows through the boroughs Bromley, Lewisham and Greenwich in the middle. The Cray goes from Bromley to Bexley in the east and joins River Darent, flowing north into the Thames between Crayford Marshes and Dartford Marshes. There are also two downstream catchments, Marsh Dykes and Greenwich, with flat slopes next to the River Thames. 4

5 Figure 2 Terrain elevations and main tributary systems A synthetic 10-year period of rainfall data was generated using the combined STNSRP and the disaggregation model. Out of the 10-year series (87,648 hours) sample data, 86% of the duration (75,490 hours) were dry without any precipitation for all sites, and the rest 14% (12,158 hours) can be broken down into 3,914 events. There were 3,719 out of these events have a total depth less than the design rainfall with 15-minute of 1 in 2 year return period. The low rainfall intensities of such events would not cause damage and flood modelling was not required. The filtering procedure then selected 11 rainfall events from the 3,914 events present in the 10 year synthetic rainfall data set. The unselected events may result in minor flooding which are negligible, comparing to the EWEs, in terms of duration and magnitude. The selected events were used as inputs to UIM for flood modelling. For each event, UIM simulates the propagation of surface water flooding caused by heavy rainfall. Figure 3 shows the manmade and the natural surface areas determined by the land features from the Ordnance Survey MasterMap. The manmade surface including buildings, structures, roads, and rails is considered having sewer systems and the 60-minute 1 in 2 year storm intensity was used as drainage capacity in UIM. The rest area is further classified by using the National Soil Map (NATMAP). Ten Hydrology of Soil Types (HOST) soil type descriptions are determined for the natural surface to set the soil infiltration rate. Outputs are the maximum depths reached by flooding during the event. 5

6 HOST Soil type description 0 Water 1 Free draining permeable soils on chalk and chalky substrates with relatively high permeability and moderate storage capacity. 3 Free draining permeable soils on soft sandstone substrates with relatively high permeability and high storage capacity. 5 Free draining permeable soils in unconsolidated sands or gravels with relatively high permeability and high storage capacity. 8 Free draining permeable soils in unconsolidated loams or clays with groundwater at less than 2m from the surface. 9 Soils seasonally waterlogged by fluctuating groundwater and with relatively slow lateral saturated conductivity. 10 Soils seasonally waterlogged by fluctuating groundwater and with relatively rapid lateral saturated conductivity. 16 Relatively free draining soils with a moderate storage capacity over slowly permeable substrates with negligible storage capacity. 18 Slowly permeable soils with slight seasonal waterlogging and moderate storage capacity over slowly permeable substrates with negligible storage. 25 Slowly permeable, seasonally waterlogged soils over impermeable clay substrates with no storage capacity. Figure 3 The manmade surface and the HOST of natural surface areas [Mapping derived from soils data Cranfield University (NSRI) and for the Controller of HMSO 2009] Figure 4 shows the maximum simulated flood depths of one event. The surface runoff concentrates to main tributaries in upstream catchments, but spreads widely on ground surface in downstream areas with flat bed slopes. Discontinuous flow paths are observed along tributaries where channels are covered by manmade structures and sudden changes of elevation in the DTM result in ponding, A further investigation of DEM data, Ordnance Survey MasterMap and satellite images is required to set up proper hydraulic connections for consequent flood modelling. The results are averaged by postcode area, as shown in Figure 5, to help end users understand the spatial relationships between flooding areas and communities, and for further socio-economic analysis. Compared to the original modelling data, shown in Figure 6, the averaged data have good agreement for small postcode areas in terms of depth and extent, but for large postcode areas with severe local flooding, the flood extents either disappear because of the reduced average depth or expand to cover whole postcode areas. These results show that a more sophisticated aggregation technique is required for large postcode areas to describe the flooding features. Figure 7 shows the average flood depths in Hither Green and Catford, which contain many rail stations. The rail line connecting Catford and Lower Sydenham is prone to flooding when the nearby River Pool (a tributary of River Ravensbourne) is overloaded under extreme rainfall conditions. The interruption of transport networks can impede emergency response, thereby worsening flood damage. The development of mitigation measures for those affected rail lines are recommended to enhance emergency response plans and community resilience against flood disasters. 6

7 Figure 4 The flood modelling result of a selected event Figure 5 The averaged flood depth by postcode area 7

8 Figure 6 The original modelling depth (left) and the averaged depth by postcode area (right) Figure 7 The averaged flood depth of postcode area and rail stations in Hither Green and Catford CONCLUSIONS An event filter has been designed for selecting from a time series of rainfall data those rainfall events that may cause pluvial flooding. Once isolated, these events are passed through a two-dimensional surface flooding model that can be driven by spatially and temporally varying rainfall inputs. A simple approach is used to account for the capacity of sewers for removing rainfall during filtering and simulation. This approach models the drainage network as a soil with infinite storage capacity. The paper also demonstrates the link of flood modelling outputs with socio-economic studies and indicates that the improvement are required in order to achieve better presentation of outcomes. ACKNOWLEDGEMENT The research presented in this paper is supported by the EPSRC funded SWERVE - Severe Weather Events Risk and Vulnerability Estimator programme package (Grant EP/F037422/1) under the CREW - Community Resilience to Extreme Weather project (Grant EP/F036795/1). Thanks are due to the Ordnance Survey and the National Soil Resource Institute for the provision of digital map data and soil type data respectively. REFERENCES Bates, B., Kundzewicz, Z.W., Wu, S. and Palutikof, J., Climate Change and Water, Intergovernmental Panel on Climate Change, Geneva. Bernstein, L. et al., Climate Change Synthesis Report, Intergovernmental Panel on Climate Change, Geneva. 8

9 British Standards Institution, Drain and sewer systems outside buildings - Part 4: Hydraulic design and environmental considerations, British Standards Institution. Burton, A., Kilsby, C.G., Fowler, H.J., Cowpertwait, P.S.P. and O'Connell, P.E., RainSim: A spatial-temporal stochastic rainfall modelling system. Environmental Modelling & Software, 23(12): Chen, A.S., Djordjević, S., Leandro, J. and Savić, D., The urban inundation model with bidirectional flow interaction between 2D overland surface and 1D sewer networks, NOVATECH 2007, Lyon, France, pp Cowpertwait, P.S.P., Lockie, T. and Davis, M.D., A spatial-temporal stochastic disaggregation model for rainfall. Res. Lett. Inf. Math. Sci., 6: Department of Environment, Design and Analysis of Urban Storm Drainage: The Wallingford Procedure. National Water Council. Djordjević, S., Prodanović, D., Maksimović, C., Ivetić, M. and Savić, D., SIPSON - Simulation of interaction between pipe flow and surface overland flow in networks. Wat. Sci. & Tech., 52(5): Environment Agency, Managing Flood Risk - Thames Region Catchment Flood Management Plan, summary document, Environment Agency, Bristol. Environment Agency, Adapting to climate change. In: U. Environment Agency (Editor), Reading. Kilsby, C.G. et al., A daily weather generator for use in climate change studies. Environmental Modelling & Software, 22(12): Mayor of London, Regional Flood Risk Appraisal (RFRA) for the London Plan - Draft London Plan Implementation Report, Greater London Authority, London. Pitt, M., The Pitt Review: Lessons learned from the 2007 floods, Cabinet Office, London. Stern, N., Stern Review on the Economics of Climate Change, Cabinet Office - HM Treasury, Cambridge. 9